Image Ranking via Hash Lookup Tables
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Solution Overview
Problem
Conventional information retrieval systems face challenges in efficiently ranking images based on relevance and similarity to queries, as they often require large storage space and computational resources for feature vectors, and struggle to learn complex decision boundaries.
Innovation Solution
The system uses a ranking model trained on ordered pairs of images, converting image features into hash values, which are then used to calculate scores through a lookup table, allowing for efficient storage and computation, and enabling the learning of non-linear decision boundaries by using hash functions to generate image hashes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature vectors are used for image ranking, then ranking accuracy can be improved, but storage space and computational resources requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information from image features by converting them into hash values. Instead of storing and processing complete feature vectors, the system extracts and stores only the hash representations, which capture the essential characteristics while dramatically reducing data size and storage requirements.
Solution Approach 2:
The patent creates a simplified copy of the image features in the form of hash values. These hash copies retain the essential information needed for ranking while being much more compact than the original feature vectors, enabling efficient storage and computation without significant loss of ranking accuracy.
2Measurement precision
If feature vectors are used for image ranking, then ranking accuracy can be improved, but computational resources and processing time increase
Solution Approach 1:
The patent extracts only the essential information from image features by converting them into hash values. Instead of storing and processing complete feature vectors, the system extracts and stores only the hash representations, which capture the essential characteristics while dramatically reducing data size and storage requirements.
Solution Approach 2:
The patent changes the representation parameters from continuous feature vectors to discrete hash values. This parameter transformation enables more efficient computational processing, as hash values can be compared and processed more quickly than floating-point feature vectors, reducing computational resource requirements while maintaining ranking accuracy.
3Adaptability or versatility
If conventional ranking models are used, then basic ranking functionality is provided, but complex decision boundaries cannot be learned
Solution Approach 1:
The patent incorporates feedback mechanisms through ordered pairs of images during training. The system receives feedback about which images should be ranked higher and uses this information to adjust the ranking model, enabling it to learn complex decision boundaries by iteratively improving its performance based on ranking feedback.
Solution Approach 2:
The patent creates a dynamic ranking model that can adapt to different image pairs and ranking scenarios. The model learns from ordered pairs of images and adjusts its decision boundaries dynamically, enabling it to handle complex ranking tasks while maintaining manageable model complexity through efficient hash-based representations.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an image ranking model to rank images based on hashes of their contents using a lookup table. An image training set is received. An image ranking model is trained with the training set by generating an image hash for each image of the ordered pair of images based on one or more features extracted from the image, computing a first score for a first image hash of a first image of the pair and a second score for a second image hash of a second image of the pair using the image ranking model, determining whether to update the image ranking model based on the first score and the second score, and updating the image ranking model using an update value based on the first score and the second score.